2 Weeks
Data Labelling and Processing
Data Engineer
Summary
This project involved developing an AI-powered customer behavior analysis system for an e-commerce company. The client needed an intelligent system to analyze customer behavior, identify patterns, and provide actionable insights to increase online purchase rates. The main problem was the low conversion rate despite high website traffic, indicating a need for deeper understanding and optimization of the customer journey.
Business Impact
Predicting the probability of both the purchase intent and actual purchase helped our client to prioritize calls queue and reduce the influence of bad traffic on the business by 16%.
The solution also helped to optimize the customer journey on the website to prevent revenue losses from unrealized purchases.
Tech challenges
By using deep learning, our team first aimed to understand customer behavior and then model the probability of purchase based on user’s web-surfing experience.
Those recommendations had to be revenue driven, maximizing profits of the service, while providing high quality services to a customer.
Integration of our Apache Spark + Tensorflow architecture with clients Elastic + PostgreSQL + RabbitMQ.
Timelines
1
2
1 Week
Solution Architecture Design
Solution Architect
3
2 Weeks
Hypothesis Generation & Validation
Deep Learning Researcher
4
1 Week
Architecture Modelling
Deep Learning Researcher
5
3 Weeks
Feature Engineering
Deep Learning Engineer, Deep Learning Researcher
6
2 Weeks
Data Streaming Pipeline Development
Data Engineer
7
6 Weeks
Training & Tuning Cycle
Deep Learning Researcher
8
2 Weeks
Integration & Deployment
Backend Developer, Dev Ops
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